Project - New Approaches to Spatial Distribution Dynamics

nsf PI: Sergio Rey Jul 31, 2014 - Aug 31, 2018 Award Link

Project summary

This research project will improve understanding of spatial inequality dynamics through methodological advances in measurement and modeling. Understanding the nature of spatial inequality dynamics is vital to both basic social science and to public policy, yet existing methods and models provide incomplete views of these spatial dynamics. While a central focus of inequality research has been on the evolution of the aggregate income distribution, much less attention has been directed at the spatial pattern of inequality and pattern dynamics. Spatial inequalities can have important implications for social cohesion, economic growth, and the design of policies targeted at reducing the level of inequality. The advances produced in the project will have wide applicability. In addition to spatial income inequality dynamics, many other social and economic phenomena have distributions that evolve in space and time. Software packages will be delivered as open-source projects and accompanied with extensive tutorials and documentation to facilitate broad dissemination across the social sciences.

Project aims

This research project will develop new analytical methods for the study of spatial dynamics of income inequality; specifically, new approaches will be developed to measuring changes in the distributional characteristics of those dynamics that incorporate their spatial dependence and heterogeneity. The new approaches will include both global measures that report summary properties of the spatial dynamics as well as local indicators that can be used to identify hot-spots of locations that are important drivers of the overall dynamics or are outliers from the global trends. Analytical and simulation based evaluations of the statistical properties of the new measures will be conducted, and empirical applications involving regional income inequalities will be carried out. These new analytics will be incorporated into enhanced versions of two open-source spatial analysis packages: Python Spatial Analysis Library and Space-Time Analysis of Regional Systems. The former provides social scientists who wish to develop custom applications with access to a modular library that can be used in conjunction with existing software to enable the new space-time analytics. The latter is a user-friendly analytical and visualization package that can facilitate exploratory investigation of spatial distribution dynamics.

Software

Open source Python package GeospatIal Distribution DYnamics (giddy).

Publications

  1. Daniel Arribas-Bel, Thomas de Graaff, and Sergio J. Rey. Looking at John Snow’s cholera map from the twenty first century: a practical primer on reproducibility and open science. Advances in Spatial Science, pages 283–306, 2017. doi:10.1007/978-3-319-50590-9_17.
  2. Stephen D. Clark and Sergio Rey. Temporal dynamics in local vehicle ownership for Great Britain. Journal of Transport Geography, 62:30–37, 2017. doi:10.1016/j.jtrangeo.2017.05.007.
  3. Chao Fan, Soe W. Myint, Sergio J. Rey, and Wenwen Li. Time series evaluation of landscape dynamics using annual Landsat imagery and spatial statistical modeling: evidence from the Phoenix metropolitan region. International Journal of Applied Earth Observation and Geoinformation, 58(Supplement C):12–25, 2017.
  4. Chao Fan, Sergio J. Rey, and Soe W. Myint. Spatially filtered ridge regression (SFRR): a regression framework to understanding impacts of land cover patterns on urban climate. Transactions in GIS, 21(5):862–879, Sep 2017. doi:10.1111/tgis.12240.
  5. Janet Franklin and Sergio J. Rey. Heterogeneous tree recruitment following disturbance in insular tropical forest, Kingdom of Tonga. Journal of Tropical Ecology, 32(6):536–542, November 2016. doi:10.1017/S0266467416000456.
  6. Randall Jackson, Sergio J. Rey, and Péter Járosi. Object orientation, open regional science, and cumulative knowledge building. Advances in Spatial Science, pages 259–282, 2017. doi:10.1007/978-3-319-50590-9_16.
  7. Wei Kang and Sergio J. Rey. Conditional and joint tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. The Annals of Regional Science, 61(1):73–93, Jul 2018. doi:10.1007/s00168-017-0859-9.
  8. Jason Laura and Sergio J. Rey. Spatial data analytics on heterogeneous multi- and many-core parallel architectures. In S. Shekhar and H. Xiong, editors, Encyclopedia of GIS, pages 1972–1981. Springer, 2017.
  9. Sergio J Rey, Luc Anselin, Xun Li, Robert Pahle, Jason Laura, Wenwen Li, and Julia Koschinsky. Open Geospatial Analytics with PySAL. ISPRS International Journal of Geo-Information, 4(2):815–836, 2015.
  10. Sergio J. Rey. Discrete regional distribution dynamics revisited. Journal of Regional and Urban Economics, 1-2:83–104, 2015.
  11. Sergio J. Rey. Bells in space: The spatial dynamics of US interpersonal and interregional income inequality. International Regional Science Review, pages 1–31, 2016.
  12. Sergio J. Rey. Space-time patterns of rank concordance: local indicators of mobility association with application to spatial income inequality dynamics. Annals of the American Association of Geographers, 106(4):788–803, Apr 2016. doi:10.1080/24694452.2016.1151336.
  13. Sergio J. Rey. Python for GIS. In John Wilson, editor, Geographic information science & technology body of knowledge. University Consortium for Geographic Information Science, 2017.
  14. Sergio J. Rey. Code as text: open source lessons for geospatial research and education. In Jean-Claude Thill and Suzana Dragicevic, editors, Geocomputational analysis and modeling of regional systems, pages 7–21. Springer, 2018.
  15. Sergio J. Rey, Wei Kang, and Levi J. Wolf. The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. Journal of Geographical Systems, 18(4):377–398, Sep 2016. doi:10.1007/s10109-016-0234-x.
  16. Sergio J. Rey and Myrna L. Sastré Gutiérrez. Comparative spatial inequality dynamics: the case of Mexico and the United States. Applied Geography, 61:70–80, Jul 2015. doi:10.1016/j.apgeog.2015.01.012.
  17. Levi J. Wolf and Sergio J. Rey. On the lumpability of regional income convergence. Letters in Spatial and Resource Sciences, 9(3):265–275, September 2015. doi:10.1007/s12076-015-0156-0.

NSF Project - New Approaches to Spatial Distribution Dynamics